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A machine Learning-Based Approach to Discriminating Basaltic Tectonic Settings

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2022年第2期21卷 2250012-2250012页

作者： Liu, Baoshun Shi, Junxia Univ Sci & Technol Beijing Sch Civil & Resource Engn Beijing 100083 Peoples R China

The geochemical characteristics of magmatic rocks can distinguish the tectonic setting of magma formation and their geochemical signatures are discriminated by using the whole-rock geochemical data. As a new attempt of artificial intelligence technology in geochemistry, the machine learning discrimination method is gradually complementary to the classical discriminative graphical method. However, the feature selection of high-dimensional data and the determination of many unknown parameters are the two main factors affecting the classification accuracy of the algorithm. In this paper, a particle swarm optimized support vector machine (PSO-SVM) model is established to classify the tectonic environments of basaltic rocks in the GEOROC database. The model mainly relies on the powerful search capability of the particle swarm algorithm to find the best parameter combination selected by the SVM based on experience to improve the accuracy. In this study, based on the basalt samples in the database and the confusion matrix, the performance of PSO-SVM model is evaluated by simulation experiments. The results show that the model proposed in this paper is more effective in distinguishing the basaltic tectonic environments, with an accuracy of more than 90%. Therefore, compared with the traditional discriminant map method, the machine learning method based on the fusion of two algorithms performs better in the tectonic environment classification problems.

关键词： Basalt tectonic settings discrimination support vector machine algorithm particle swarm optimization

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Using machine learning algorithms to guide rehabilitation planning for home care clients

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2007年第1期7卷 41-41页

作者： Zhu, Mu Zhang, Zhanyang Hirdes, John P. Stolee, Paul Univ Waterloo Dept Stat & Actuarial Sci Waterloo ON N2L 3G1 Canada Univ Waterloo Dept Hlth Studies & Gerontol Waterloo ON N2L 3G1 Canada Homewood Hlth Ctr Homewood Res Inst Guelph ON Canada Univ Waterloo Sch Optometry Waterloo ON N2L 3G1 Canada Univ Waterloo Res Inst Aging Waterloo ON N2L 3G1 Canada

Background: Targeting older clients for rehabilitation is a clinical challenge and a research priority. We investigate the potential of machine learning algorithms - support vector machine (SVM) and K-Nearest Neighbors (KNN) - to guide rehabilitation planning for home care clients. Methods: This study is a secondary analysis of data on 24,724 longer-term clients from eight home care programs in Ontario. Data were collected with the RAI-HC assessment system, in which the Activities of Daily Living Clinical Assessment Protocol (ADLCAP) is used to identify clients with rehabilitation potential. For study purposes, a client is defined as having rehabilitation potential if there was: i) improvement in ADL functioning, or ii) discharge home. SVM and KNN results are compared with those obtained using the ADLCAP. For comparison, the machine learning algorithms use the same functional and health status indicators as the ADLCAP. Results: The KNN and SVM algorithms achieved similar substantially improved performance over the ADLCAP, although false positive and false negative rates were still fairly high (FP > .18, FN > .34 versus FP > .29, FN. > .58 for ADLCAP). Results are used to suggest potential revisions to the ADLCAP. Conclusion: machine learning algorithms achieved superior predictions than the current protocol. machine learning results are less readily interpretable, but can also be used to guide development of improved clinical protocols.

关键词： support vector machine machine Learning algorithm support vector machine Model Original Scale support vector machine algorithm

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Behavioral signatures related to genetic disorders in autism

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MOLECULAR AUTISM 2014年第1期5卷 11-11页

作者： Bruining, Hilgo Eijkemans, Marinus J. C. Kas, Martien J. H. Curran, Sarah R. Vorstman, Jacob A. S. Bolton, Patrick F. Univ Med Ctr Dept Psychiat Brain Ctr Rudolf Magnus NL-3508 GA Utrecht Netherlands Brain Ctr Rudolf Magnus Dept Translat Neurosci Utrecht Netherlands Univ Med Ctr Julius Ctr Hlth Sci & Primary Care Utrecht Netherlands Kings Coll London Inst Psychiat London WC2R 2LS England

Background: Autism spectrum disorder (ASD) is well recognized to be genetically heterogeneous. It is assumed that the genetic risk factors give rise to a broad spectrum of indistinguishable behavioral presentations. Methods: We tested this assumption by analyzing the Autism Diagnostic Interview-Revised (ADI-R) symptom profiles in samples comprising six genetic disorders that carry an increased risk for ASD 22q11.2 deletion, Down's syndrome, Prader-Willi, supernumerary marker chromosome 15, tuberous sclerosis complex and Klinefelter syndrome;total n = 322 cases, groups ranging in sample sizes from 21 to 90 cases). We mined the data to test the existence and specificity of ADI-R profiles using a multiclass extension of support vector machine SVM) learning. We subsequently applied the SVM genetic disorder algorithm on idiopathic ASD profiles from the Autism Genetics Resource Exchange AGRE). Results: Genetic disorders were associated with behavioral specificity, indicated by the accuracy and certainty of SVM predictions;one-by-one genetic disorder stratifications were highly accurate leading to 63% accuracy of correct genotype prediction when all six genetic disorder groups were analyzed simultaneously. Application of the SVM algorithm to AGRE cases indicated that the algorithm could detect similarity of genetic behavioral signatures in idiopathic ASD subjects. Also, affected sib pairs in the AGRE were behaviorally more similar when they had been allocated to the same genetic disorder group. Conclusions: Our findings provide evidence for genotype-phenotype correlations in relation to autistic symptomatology. SVM algorithms may be used to stratify idiopathic cases of ASD according to behavioral signature patterns associated with genetic disorders. Together, the results suggest a new approach for disentangling the heterogeneity of ASD.

关键词： Autism Spectrum Disorder support vector machine Autism Spectrum Disorder Autism Spectrum Disorder Symptom support vector machine algorithm

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MicroRNA expression in multiple myeloma is associated with genetic subtype, isotype and survival

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BIOLOGY DIRECT 2011年第1期6卷 23-23页

作者： Chi, Jianxiang Ballabio, Erica Chen, Xiao-He Kusec, Rajko Taylor, Steve Hay, Deborah Tramonti, Daniela Saunders, Nigel J. Littlewood, Timothy Pezzella, Francesco Boultwood, Jacqueline Wainscoat, James S. Hatton, Christian S. R. Lawrie, Charles H. Univ Oxford John Radcliffe Hosp Nuffield Dept Clin Lab Sci Oxford OX3 9DU England Ctr Study Haematol Malignancies Nicosia Cyprus Univ Zagreb Dubrava Univ Hosp Zagreb 41000 Croatia Univ Zagreb Zagreb Sch Med Zagreb 41000 Croatia Univ Oxford Weatherall Inst Mol Med Computat Biol Res Grp Oxford England John Radcliffe Hosp Dept Haematol Oxford OX3 9DU England Univ Oxford Sir William Dunn Sch Pathol Oxford OX1 3RE England

Background: MicroRNAs are small RNA species that regulate gene expression post-transcriptionally and are aberrantly expressed in many cancers including hematological malignancies. However, the role of microRNAs in the pathogenesis of multiple myeloma (MM) is only poorly understood. We therefore used microarray analysis to elucidate the complete miRNome (miRBase version 13.0) of purified tumor (CD138(+)) cells from 33 patients with MM, 5 patients with monoclonal gammopathy of undetermined significance (MGUS) and 9 controls. Results: Unsupervised cluster analysis revealed that MM and MGUS samples have a distinct microRNA expression profile from control CD138+ cells. The majority of microRNAs aberrantly expressed in MM (109/129) were up-regulated. A comparison of these microRNAs with those aberrantly expressed in other B-cell and T-cell malignancies revealed a surprising degree of similarity (similar to 40%) suggesting the existence of a common lymphoma microRNA signature. We identified 39 microRNAs associated with the pre-malignant condition MGUS. Twenty-three (59%) of these were also aberrantly expressed in MM suggesting common microRNA expression events in MM progression. MM is characterized by multiple chromosomal abnormalities of varying prognostic significance. We identified specific microRNA signatures associated with the most common IgH translocations (t(4;14) and t(11;14)) and del(13q). Expression levels of these microRNAs were distinct between the genetic subtypes (by cluster analysis) and correctly predicted these abnormalities in > 85% of cases using the support vector machine algorithm. Additionally, we identified microRNAs associated with light chain only myeloma, as well as IgG and IgA-type MM. Finally, we identified 32 microRNAs associated with event-free survival (EFS) in MM, ten of which were significant by univariate (logrank) survival analysis. Conclusions: In summary, this work has identified aberrantly expressed microRNAs associated with the diagn

关键词： Multiple Myeloma microRNA Expression Event Free Survival support vector machine algorithm Genetic Subtype

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Exploration on Portfolio Selection and Risk Prediction in Financial Markets Based on SVM algorithm

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY AND WEB ENGINEERING 2023年第1期18卷 1页

作者： Han, Xinyu Yao, Dianqi Harbin Univ Commerce Harbin Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China

In order to cope with the complex risk environment of the current financial market, achieve portfolio optimization and accurate risk prediction, this paper conducts effective research using SVM algorithm. This article uses stock data as a sample to empirically analyze the risk return and risk prediction performance of investment portfolio strategies based on SVM algorithm. Compared with traditional index fund investment strategies, the risk resistance of investment portfolio strategies is significantly improved, and the risk return is also stable at a high level. In addition, with the support of SVM algorithm, the risk prediction error level in the financial market remains within a relatively low range. From the perspective of practical applications, the financial market investment portfolio selection and risk prediction based on SVM algorithm has strong feasibility.

关键词： Financial Market Portfolio Selection Risk Prediction support vector machine algorithm

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A New Approach in Detectability of Microcalcifications in the Placenta during Pregnancy Using Textural Features and K-Nearest Neighbors algorithm

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JOURNAL OF IMAGING 2022年第3期8卷 81-81页

作者： Miron, Mihaela Moldovanu, Simona Stefanescu, Bogdan Ioan Culea, Mihai Pavel, Sorin Marius Culea-Florescu, Anisia Luiza Dunarea de Jos Univ Galati Fac Automat Comp Elect Engn & Elect Dept Comp Sci & Informat Technol 47 Domneasca Str Galati 800008 Romania Dunarea de Jos Univ Galati Fac Med & Pharm Dept Clin Surg 47 Domneasca Str Galati 800008 Romania Clin Emergency Hosp Sf Ap Andrei Galati Dept Obstet & Gynecol Galati 800578 Romania Dunarea de Jos Univ Galati Fac Automat Comp Elect Engn & Elect Dept Elect & Telecommun 47 Domneasca Str Galati 800008 Romania

(1) Background: Ultrasonography is the main method used during pregnancy to assess the fetal growth, amniotic fluid, umbilical cord and placenta. The placenta's structure suffers dynamic modifications throughout the whole pregnancy and many of these changes, in which placental microcalcifications are by far the most prominent, are related to the process of aging and maturation and have no effect on fetal wellbeing. However, when placental microcalcifications are noticed earlier during pregnancy, they could suggest a major placental dysfunction with serious consequences for the fetus and mother. For better detectability of microcalcifications, we propose a new approach based on improving the clarity of details and the analysis of the placental structure using first and second order statistics, and fractal dimension. (2) Methods: The methodology is based on four stages: (i) cropping the region of interest and preprocessing steps;(ii) feature extraction, first order-standard deviation (SD), skewness (SK) and kurtosis (KR)-and second order-contrast (C), homogeneity (H), correlation (CR), energy (E) and entropy (EN)-are computed from a gray level co-occurrence matrix (GLCM) and fractal dimension (FD);(iii) statistical analysis (t-test);(iv) classification with the K-Nearest Neighbors algorithm (K-NN algorithm) and performance comparison with results from the support vector machine algorithm (SVM algorithm). (3) Results: Experimental results obtained from real clinical data show an improvement in the detectability and visibility of placental microcalcifications.

关键词： placenta calcifications preterm placental calcifications first order feature second order feature t-test K-Nearest Neighbors algorithm support vector machine algorithm

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Diagnosis of drug-induced renal tubular toxicity using global gene expression profiles

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JOURNAL OF TRANSLATIONAL MEDICINE 2007年第1期5卷 47-47页

作者： Jiang, Ying Gerhold, David L. Holder, Daniel J. Figueroa, David J. Bailey, Wendy J. Guan, Ping Skopek, Thomas R. Sistare, Frank D. Sina, Joseph F. Merck & Co Inc Merck Res Labs Safety Assessment West Point PA 19486 USA Merck & Co Inc Merck Res Labs Biometr Res West Point PA 19486 USA

Toxicogenomics can measure the expression of thousands of genes to identify changes associated with drug induced toxicities. It is expected that toxicogenomics can be an alternative or complementary approach in preclinical drug safety evaluation to identify or predict drug induced toxicities. One of the major concerns in applying toxicogenomics to diagnose or predict drug induced organ toxicity, is how generalizable the statistical classification model is when derived from small datasets? Here we presented that a diagnosis of kidney proximal tubule toxicity, measured by pathology, can successfully be achieved even with a study design of limited number of training studies or samples. We selected a total of ten kidney toxicants, designed the in life study with multiple dose and multiple time points to cover samples at doses and time points with or without concurrent toxicity. We employed SVM (support vector machine) as the classification algorithm for the toxicogenomic diagnosis of kidney proximal tubule toxicity. Instead of applying cross validation methods, we used an independent testing set by dividing the studies or samples into independent training and testing sets to evaluate the diagnostic performance. We achieved a Sn (sensitivity) = 88% and a Sp (specificity) = 91%. The diagnosis performance underscores the potential application of toxicogenomics in a preclinical lead optimization process of drugs entering into development.

关键词： support vector machine support vector machine Model Charles River Laboratory Microarray Gene Expression Data support vector machine algorithm

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PredictingβαβMotifs Based on SVM algorithm by Using the ID and MS values

PredictingβαβMotifs Based on SVM Algorithm by Using the I...

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第五届全国生物信息学与系统生物学学术大会暨国际生物信息学前沿研讨会高通量时代的生物信息学与系统生物学

作者： Lixia Sun Xiuzhen Hu College of Sciences Inner Mongolia University of Technology

Background:βαβmotif is an important super secondary structure in *** strand- loop-helix-loop-strand structures,if two parallelβ-strands are connected by a a-helix,and there are one or more hydrogen bonds between two adjacent strands,then the structure is called asβαβmotifs,otherwise it is considered as non-βαβ*** function of proteins is closely related with their structure,thus study of protein structure is very important to understand its *** the high-throughput era,by using the theoretical method to predict protein structure has become one of the important ways in biology *** is very difficult to directly predict the tertiary structure from *** secondary structure, especiallyβαβmotif,is a bridge between secondary structure and tertiary structure.βαβmotifs often appear in the bacillus subtilis protease,and many function sites occur in theβαβmotifs,therefore,prediction ofβαβmotif has very important *** provides theoretical direction for drug molecules design. Methods:We constructed a new dataset,which contains 4277βαβmotifs and 3366 nonβαβmotifs with sequence identity <25%and resolution < 3.0 *** vector machine (SVM) algorithm is used to predictβαβmotif by using increment of diversity(ID) values, matrix scoring(MS) values and amino acids component as parameters. Results:In this paper,by using the ID values,MS values and amino acids component to express the sequence information,then we combined these parameters as SVM input. The predictive performance is *** overall accuracy and Matthew's correlation coefficient of 5-fold cross-validation achieve 77.7%and 0.527,the sensitivity ofβαβmotifs and non-βαβmotifs achieve respectively 83.6%and 68.4%,the specificity ofβαβmotifs and non-βαβmotifs achieve respectively 79.1%and 74.4%. Conclusions:The MS values can reflect the conservatism of the amino acids sequence, and ID value is the sequence information's secondary *** SVM algorithm is a convex quadrati

关键词： βαβmotifs amino acids component increment of diversity scoring matrix support vector machine algorithm

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Predicting βαβ Motifs Based on SVM by Using the ID and MS values

Predicting βαβ Motifs Based on SVM by Using the ID and MS...

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International Conference on Biomedical Engineering and Informatics

作者： Lixia Sun Xiuzhen Hu Shaobo Li College of Sciences Inner Mongolia University of Technology Huhhot China

ISBN: (纸本)9781467311830

From the Protein Data Bank (PDB), we screened out 1635 sequences with identity <25% and resolution < 3.0 ?. Each sequence contains at least one βαβ motif. This new dataset contains 4277 βαβ motifs and 3366 non-βαβ motifs. We fixed sequence length for βαβ motifs. By using the parameters with increment of diversity (ID) values, matrix scoring (MS) values and amino acids component to express the information of sequence, a support vector machine algorithm for predicting βαβ motifs was proposed. The overall accuracy and Matthew's correlation coefficient of 5-fold cross-validation achieved 77.7% and 0.527.

关键词： βαβ motifs increment of diversity scoring matrix amino acids component support vector machine algorithm

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support vectors machine-based model for diagnosis of multiple sclerosis using the plasma levels of selenium, vitamin B₁₂, and vitamin D₃

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Informatics in Medicine Unlocked 2020年 20卷

作者： Sharifmousavi, Seyed Sajjad Borhani, Matia Sadat Biomedical Engineering Department Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Biology Department Faculty of Sciences Gonbad Kavous University Golestan Iran

Background: It has been reported that some nutritional factors can play a significant role in immune system regulation and the etiology of immune-mediated diseases such as multiple sclerosis (MS). However, there have been no studies on the relationship between nutritional factors and MS as a primary diagnostic method. Methods: Plasma samples of 99 MS patients and 81 healthy people as the control group were assayed using atomic absorption spectroscopy and the chemical autoanalyzer method to determine the amount of selenium and vitamins (B₁₂, D₃), respectively. To find a diagnostic model, three types of supervised machine learning methods, i.e., support vector machine algorithm (SVM), decision tree (DT), and K-nearest neighbor (KNN) were used. Results: The diagnostic model based on the support vector machine method had high accuracy (98.89%), sensitivity (98.98%), and positive predictive value (98.98%). Moreover, the SVM had a true positive rate of 99.9%. Conclusion: The results of this study showed that the SVM algorithm with Radial Basis Function Kernel could be effective in obtaining a good MS diagnostic method based on the plasma levels of selenium, vitamin B₁₂, and vitamin D₃ regarding DT and KNN. This method is non-invasive and cost-effective and can be used as a primary screening method for the diagnosis of MS. © 2020

关键词： Multiple sclerosis Selenium support vector machine algorithm Vitamin B12 Vitamin D3

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